spatial and temporal changes of sundarbans reserve forest ... · sundarbans is located south of the...

Environment and Natural Resources Journal 2017; 15(1): 51-61

Spatial and Temporal Changes of Sundarbans Reserve Forest in

Bangladesh

Sanaul Haque Mondal1,* and Premanondo Debnath2

1,*Department of Social Relations, East West University, Aftabnagar, Dhaka, Bangladesh

2Center for Environmental and Geographic Information Services (CEGIS), Dhaka, Bangladesh

ARTICLE INFO ABSTRACT

Received: 19 Dec 2016

Received in revised:

4 Mar 2017

Accepted: 7 Mar 2017

Published online:

23 Mar 2017

DOI: 10.14456/ennrj.2017.5

Sundarbans, the largest mangrove chunk of the world is shared between

Bangladesh (62%) and India (38%). The objective of this paper was to

examine the spatial and temporal changes in land cover (forest cover area) of

Sundarbans from 1973 to 2010 using Remote Sensing and Geographic

Information System (GIS) tool. Normalized Difference Vegetation Index

(NDVI) was applied to calculate the density of vegetation of Sundarbans

Reserved Forest (SRF). This study found that there was no major changes in

total areas of SRF in the last 37 years (from 1973 to 2010) albeit changes were

detected within the four land cover categories- water body, mudflat, barren

land and vegetated land. During 1973 to 2010, water bodies, mudflats and

barren lands increased by 0.45%, 19.69% and 14.81% respectively; while

vegetated land decreased by 4.01% during the same period. This indicated that

density of evergreen vegetation and its canopy closure decreased in

Sundarbans. This paper was thus recommended to develop GIS and remote

sensing based real time monitoring system to identify spatial and temporal

changes of land cover classes of SRF.

Keywords:

Bangladesh / Geographic

Information System (GIS) /

Land cover change / Remote

Sensing / Sundarbans

Reserved Forest /

Sundarbans Mangrove

Forest

* Corresponding author:

E-mail:

[email protected]

1. INTRODUCTION

Mangroves are heavily used traditionally and

commercially worldwide (Alongi, 2002). These

forests are important land cover found in the tropics

and subtropics regions of the world. Mangroves may

grow as trees or shrubs according to the climate,

salinity of the water, topography and edaphic

features of the area in which they exist (FAO,

2007). These forests are also important for coastal

ecosystems in terms of primary production and

coastal environment protection. However, all over

the world mangrove ecosystems are threatened with

destruction through various forms of human

pressure, in particular extraction, pollution and

reclamation (Akhter, 2006). The mangrove areas

worldwide have dropped to some 15.2 million

hectares in 2005, down from 18.8 million hectares

in 1980 (FAO, 2007). Therefore, it is important to

assess the changes of mangrove forest cover for

improved understanding on the gradual altering

characteristics. Sundarbans mangrove forest is the

largest and contiguous mangrove block of the world,

has been gradually decreasing due to natural

processes and human interventions. Around 200

years ago, Sundarbans was around 16,700 km2

(Banglapedia, 2015a) which is now dwindling down

to half of its original size; the other half being

cleared and converted to agricultural land (Hussain

and Archarya, 1994). Therefore, the study of land

cover classes is a very useful tool to identify spatial

and chronological changes of Sundarbans mangrove

forest.

The Sundarbans Reserved Forest (SRF) is the

most economically valuable and the richest natural

forests of Bangladesh which is blessed with

enormous natural resources. Historically, these

natural resources always engrossed people for

diverse interests. This forest is surrounded by a very

densely human populated area. These frontier

people are mostly depended on Sundarbans for their

livelihoods, therefore human pressure on the forest

is important. Approximately 2.5 million people live

in the small villages surrounding the Sundarbans,

while the number of people within 20 km of the

52 Mondal SH and Debnath P / Environ. Nat. Resour. J. 2017; 15(1): 51-61

Sundarbans boundary is 3.14 million (MARC,

1995). SRF is free from any encroachment and

permanent human habitation except few hundreds of

Forest Department personnel on official duty

(IPAC, 2012). However, people like Bawalis (wood

collectors), Mouals (honey or bee wax collectors),

fishermen, mollusk shell collectors and grass cutters

converged into the forest for extracting forest

resources. Moreover, numerous people are also

engaged in legal or illegal commercial exploitation

of forest resources. The Sundarbans forest has been

divided into three zones on the basis of salinity as

follows: less saline zone (5-15 ppt) in eastern zone,

moderately saline Zone (15-25 ppt) and strong

saline zone (25-30 ppt) (CEGIS, 2006). The

concentration of salinity in Sundarbans increases

from east to west and, thus, density, vegetation

growth and canopy closure decreases from east to

west (Aziz and Paul, 2015). Level of salinity is also

important determinant for height and growth of

mangrove species (CEGIS, 2006; Hussain et al.,

2013). For example, Sundari (Heritiera fomes) trees

grows in low saline zone (5~10 ppt), Gewa

(Excoecaria agallocha) in moderate saline zone (10-

25 ppt) and Goran (Ceriops decandra) in high saline

zone (over 25 ppt) (Hussain et al., 2013).

Sundarbans forest has lost its ecological

diversity due to natural and anthropogenic causes

(Islam, 2016). The productivity of the mangrove

system had already declined by 25% in the two

decades leading up 1985 (Chaffey et al., 1985).

Sundari is the dominant floral species of the

Sundarbans. But the abundance and dominance of

Sundari tree in the forest has been decreasing

because of ‘Top-dying disease’ (Banglapedia,

2015b). Around 20.18 million Sundari trees are

seriously affected by this disease (Rahman, 1998).

On the other hand, anthropogenic pressure on

Sundarbans has been increasing in manifolds over

the last few decades. These changes will have

definite adverse impact on Sundarbans and lives and

livelihoods of frontier population. Therefore, this

research aimed to examine the spatial and temporal

changes in land cover (forest cover area) of

Sundarbans reserved forest from 1973 to 2010 using

Remote Sensing and Geographic Information

System (GIS) techniques to realize the contributing

factors of Sundarbans land cover depletion.

Rahman et al. (2013) examined effectiveness

of different classification methods for extraction of

Mangrove forest in Sundarbans using Landsat

ETM+ data. Their study suggested that band

ratio/supervised classification approach produces

the best accuracy in detecting the mangrove class.

Bhowmik and Cabral (2013) studied on identifying

the impact of cyclone Sidr on the floristic

composition of Sundarbans using remote sensing

method. This study identified a temporary lost of

diversity (in terms of relative abundance) in affected

three floristic taxa (Heritiera fomes, Excoecaria

agallocha and Sonneratia apetala) of the

Sundarbans after that severe exogenous

perturbation; which took three years to regenerate.

The study of Rahman and Begum (2013)

aimed to identify the spatial and temporal (during

1980 to 2009) dynamics of land cover and its impact

on the ecosystem in Khulna and Satkhira districts

in Bangladesh using GIS and remote sensing

techniques. They found significant increase in

homestead areas and water bodies, and decrease in

fallow lands in their targeted study area. Their study

did not find any remarkable change in mangrove

vegetation.

Emch and Peterson (2006) in their study

attempted to identify and compare change in results

using the three image processing methods:

normalized differential vegetation index (NDVI),

maximum likelihood classification, and subpixel

classification. The outcome of their study was both

NDVI and subpixel classification revealed similar

spatial distributions of areas that were deforested

from 1989 to 2000 in Sundarbans. Islam (2014)

studied on identifying the changes in vegetation

cover of Sundarbans mangrove forest (both in

Bangladesh and India) during 1975 to 2006 using

Landsat imagery. Spatial changes were not

identified even though huge population settled in the

vicinity of the forest. The mangrove forest is

decreased by 19.3% due severe tropical cyclone in

1977 and 1988. He concluded that future of

Sundarbans depends to climatic issues e.g., salinity,

frequency of severe tropical cyclone and tidal effect

those directly depend to global warming.

A study was conducted by Resources

Information Management System (RIMS) unit

under Ministry of Environment and Forest,

Bangladesh on ‘Assessment of Sundarbans

Reserved Forest in 1960, 1985, 1995 and 2013’

(MoEF, 2014). This research described occupancy

of different mangrove species in different years. The

Mondal SH and Debnath P / Environ. Nat. Resour. J. 2017; 15(1): 51-61 53

study found that areas covered by different forest

types had been decreasing at an alarming rate. The

area occupied with Sundari tree was decreased by

24% in 2013 (74264 hectares) compared with 1960

(98551 hectares). The occupancy of Sundari-Gewa

decreased from 123247 hectares in 1985 to 102274

hectares in 2013. In 1960, Sundari-Passur occupied

around 29752 hectares forest cover. But, in 1985,

this forest class reduced by 93%. Since then this

Sundari-Passur tree cover remained more or less

same with no further change. Moreover, similar

pattern also observed for Sundari-Passur-Kankra.

Sundari-Passur-Kankra forest type increased by 5%

during 1985 (6799 hectares) to 1995 (7143 hectares)

and decreased by 0.83% during 1985 (6799

hectares) to 2013 (7084 hectares). In contrast, the

area of Gewa forest type increased by 70.85% in

2013 (21454 hectares) compared to 1960 (12557

hectares). Likewise, the area covered by Keora was

almost tripled in 2013 (10603 hectares) compared to

1985 (3509 hectares).

2. METHODOLOGY

2.1 Study area

Sundarbans reserve forest in Bangladesh is

located at the edge of the southwest of Bangladesh

between the Baleswar river in East and the

Harinbanga river in west adjacent to the Bay of

Bengal (UNESCO-WHC, 2016). Geographically,

Sundarbans is located south of the Tropic of Cancer

and lies between longitude 89°00É and 89°55É

and latitudes 21°30Ń and 22°30Ń (Chaffey et al.,

1985). The location of study area is shown in Figure

1. This study was conducted on the satellite imagery

of Sundarbans reserved forest in Bangladesh.

Figure 1. Map of study area

2.2. Methodology of the study

The study used Landsat images to derived

chronological land cover maps. These images are

provided historical records at free of cost. The study

period was chosen from 1973 to 2010.

2.2.1. Collection of Landsat Images of study

area

This study was conducted applying Universal

Transverse Mercator (UTM) projected Landsat

imagery from the Glovis. Images acquired in the

months of November, December, January and

February considering vegetation phenology, and less


cloudy time periods of the year. Cloud free quality

images are very important for analyzing land cover,

hence actual study interval period was

compromised. Cloud free images were collected

(except 2009 with 10% cloud cover) for this study.

The Landsat images of SRF from 1973 to 2010 as

shown in the Landsat images of Sundarbans in

Figure 2. The actual acquisition dates of the images,

spatial resolution and associated sensors that used in

the study as shown in the properties of Landsat

images in Table 1. Data from two sensors of

Landsat- Multi Spectral Scanner (MSS) and

Thematic Mapper (TM) used to derive land cover

maps in satellite image. To visualize the images,

blue, green, red and near-infrared bands were used

for object identification. Two UTM Zone (45 and

46) projected data were downloaded from the data

hub to cover SRF.

Figure 2. Landsat images of Sundarbans from 1973 to 2010 (Source: prepared from Glovis)

Table 1. Properties of Landsat images used in this study

Sensor type Platform Path/Row Acquisition date Spatial Resolution (M) Projection

MSS LandSat 1 147/045 1973-02-02 80 UTM Zone 46




Table 1. Properties of Landsat images used in this study (cont.)

Sensor type Platform Path/Row Acquisition date Spatial Resolution (M) Projection




TM LandSat 4 137/045 1989-01-12 30 UTM Zone 46








Near Infrared (NIR) (0.7-0.8 μm for MSS and

0.76- 0.90 μm for TM) and Red (0.6-0.7 μm for

MSS and 0.63-0.69 μm for TM) bands were used to

detect the forest cover change in this study (USGS,

2016).

2.2.2. Image processing

ERDAS Imagine 2014 was used to perform

digital images processing. This study used different

bands (or layers) of information. Therefore, layers

stacking were method was applied. Geometric

Correction process used to co-registration the

images as Bangladesh Transverse Mercator (BTM)

projection. Histogram equalization was applied for

clear visualization of the object in the images. To

improve the quality of images, haze reduction tool

was used. To calculate radiance from digital number

(DN), the following formula was used.

Lλ = ((LMAXλ-LMINλ) / (QCALMAX-QCALMIN)) x

(QCAL-QCALMIN) + LMINλ

Where:

Lλ = spectral radiance

QCAL = quantized calibrated pixel value in digital

number

LMINλ= spectral radiance scales to QCALMIN

LMAXλ = spectral radiance scales to QCALMAX

QCALMIN = minimum quantized calibrated pixel value

QCALMAX = maximum quantized calibrated pixel value

After that reflectance was calculated using the

following formula (NASA, 2011):

ρp=(π ∗ Lλ ∗d2) / (ESUNλ ∗ cosθs)

Where:

ρp = Planetary reflectance

Lλ = Radiance at the sensor's aperture

d = Distance in astronomical units

ESANλ = Solar exo-atmospheric irradiances

θs = Zenith angle in degrees

2.2.3. Image classification, NDVI calculation

and map preparation

After careful visual interpretation, four

categories of land cover class scheme were developed.

These classes along with their descriptions as

presented in the land cover classification scheme in

Table 2. The images were limited to mangrove forest

area and thus only four basic thematic classes were

created: vegetated land, mudflat, barren land and

water body.

Normalized Difference Vegetation Index

(NDVI) is calculated from the visible and near-

infrared light reflected by vegetation. Thus NDVI

can be determined: near-infrared reflectance (RNIR)

minus visible reflectance (RRED) divided by near-

infrared reflectance (RNIR) plus visible reflectance

(RRED). Mathematically, the formula is (NASA,

2011):

NDVI= (RNIR –RRED)/ (RNIR+RRED)

NDVI values ranges from minus one (-1) to plus one

(+1); however, no green leaves gives a value close to zero

(NASA, 2011). A zero means no vegetation and close to

+1 indicates the highest possible density of green leaves

(NASA, 2011).

Using the conditional logic NDVI of the

Landsat images were calculated and accordingly

area covered by each category was calculated. The


NDVI raster layer classified as water body, mudflat,

barren land and vegetated land considering

vegetation cover and forest types of Sundarbans

mangrove forest. Finally, land cover maps of 1973,

1978, 1989, 2001, 2009 and 2011 were prepared

using Arc GIS 10.3. For accuracy assessment of

land cover classes, this study used only high

resolution areal images.

Table 2. Land cover classification scheme for this study

Land cover types Description

Water Body River channels, rivulets and canals in Sundarbans

Mudflat Wetlands in Sundarbans where tidal activities observed

Barren Land Exposed soils/ sandy surfaces in Sundarbans

Vegetation

This includes all types of mangrove and non-mangrove forest cover

of Sundarbans

3. RESULT AND DISCUSSION

3.1 Sundarbans land cover change: trend, rate and

magnitude

NDVI spectral indices were calculated using

Sundarbans study area subsets of 1973, 1978, 1989,

2001, 2009 and 2010 Landsat images and presented

in Table 3.

During 1973 to 2010, barren land and mudflat

of Sundarbans reserved forest increased, whereas,

vegetated land decreased significantly. Rate of

changes in land cover classes presented in Table 4.

Table 3. Summary of land cover classification statistics between 1973 and 2010 (area in hectares)

Land cover 1973 1978 1989

Area % Area % Area %

Water body 411640.5 51.12 413009.4 51.29 412123.6 51.18

Mudflat 36397.01 4.52 39054.31 4.85 39617.98 4.92

Barren land 28264.05 3.51 26089.89 3.24 27297.75 3.39

Mangrove 328942 40.85 327089.9 40.62 326204.1 40.51

Total 805243.5 100 805243.5 100 805243.5 100

Land cover 2001 2009 2010

Area % Area % Area %

Water body 403104.9 50.06 422108.64 52.42 413492.5 51.35

Mudflat 42114.23 5.23 40503.75 5.03 43563.67 5.41

Barren land 32451.31 4.03 35269.66 4.38 32451.31 4.03

Mangrove 327573.1 40.68 307361.44 38.17 315736 39.21

Total 805243.5 100 805243.5 100 805243.5 100

Source: Calculated from NDVI value (each percentage column represents percentages of total area of referenced year)

3.2 Water bodies

In 1973, water body in the study area

occupied about 411640.47 hectares of Sundarbans

forest land representing about 51.12% (Table 3) of

total land cover for the year. By 2010, the area

occupied by the water body increased marginally by

0.45% with about 805243.49 hectares representing

51.35% of total land cover for that period (Table 3).

The rate of decreased within inter-study periods was

0.21%, 2.19%, 2.30% and 2.05% during the period of

1978-1989, 1989-2001, and 2009-2010, respectively;

whereas, increased during 1973 to 1978, and 2001 to

2009 by 0.33% and 4.71%, respectively (Table 4). The

most significant increased of water bodies observed

for 2009 which was attributed by the cyclone Ayla

that hit on May 2009 and the percentage occupied

by water bodies (52.42%) was the highest during

1973 to 2010 (Table 3).


3.3 Mudflats

The percentage occupied by mudflats to the

total study was around 5.41% in 2010 that increased

from 4.52% in 1973, 4.85% in 1978, 4.92% in 1989

and 5.23% in 2001 (Table 3). Moreover, the rate of

increase during the inter-study period was also

higher for mudflats. The percentage change analysis

showed that in 2009 mudflats decreased by 3.82%

compared to 2001 (Table 4). This decrease was

triggered by the cyclone Ayla that resulted

inundation of mudflats and increase in water bodies

(Table 4).

Table 4. Rate of changes in land cover classes from 1973 to 2010 (rate changes with respect to their original

area in the respective year)

Land cover 1973 to 1978 1978 to 1989 1989 to 2001 2001 to 2009 2009 to 2010

Water Body 0.33% -0.21% -2.19% 4.71% -2.04%

Mudflat 7.30% 1.44% 6.30% -3.82% 7.55%

Barren Land -7.69% 4.63% 18.88% 8.68% -7.99%

Vegetation -0.56% -0.27% 0.42% -6.17% 2.72%

Source: Calculated from Table 3 using the equation 𝑌2−𝑌1

𝑌1𝑋100, where Y1 areas of previous year and Y2 areas of new year.

3.4. Barren lands

The most remarkable changed observed for

barren land distribution. The amount of barren land

in 1973 was 28264.05 hectares that declined

significantly in 2010 to 32451.31 hectares (Table 3).

In 1973, barren land occupied 3.51% to the total

study area that increased and constituted 4.03%,

4.38%, and 4.03% to the total study area in 2001,

2009 and 2010, respectively (Table 3). The inter-

study declined rate for barren land was also higher

than other three categories (Table 4). During 1989

and 2001, total area occupied by barren land

increased by 18.88% which was the highest growth

rate observed among all land cover categories

(Table 4).

3.5. Vegetated lands

The dense vegetation forest (included both

mangrove and non-mangrove species) area

decreased at a steady rate throughout the study

period. Vegetated area covered 328941.97 hectares

in 1973 (Table 3). It was merely dropped in 1978

and covered around 327089.91 hectares. Vegetated

land again dropped in 1989 and reached about

326204.14 hectares. Furthermore, vegetated land

decreased in 2001 and 2009 which was 327573.05

hectares and 307361.44 hectares, respectively

(Table 3). The analysis demonstrated that vegetated

land constituted 40.85%, 40.62%, 40.51%, 40.68%

and 38.17% in 1973, 1978, 1989, 2001 and 2009,

respectively to the total study area (Table 3). The

highest decline rate of vegetated land was 6.17% in

2009 when cyclone Ayla landfall (Table 4).

Moreover, regional variations in vegetation

coverage were also observed throughout the study

years. Dense vegetation was apparently distributed

uniformly in the forest except 2009 (Figure 3). In

2010 (post- cyclone Ayla), water bodies and barren

lands decreased by 2.04% and 7.99% respectively,

whereas, mudflats and vegetated land increased by

7.55% and 2.72%, respectively (Table 4).

The areas occupied by water bodies, mudflats,

barren lands, and vegetated lands from 1973 to 2010

were presented in Figure 4.


Figure 3. Land cover map of Sundarbans in different years (1973, 1978, 1989, 2001, 2009 and 2010)


Water Body Mudflats Barren Land Vegetated Land

Figure 4. Graphical presentation of areas occupied by water bodies, mudflats, barren lands, and vegetated

lands from 1973 to 2010 (calculated from NDVI)

According to FAO (2007), the Sundarbans

reserved forest is well protected, and no major

changes have been found during 1980 to 2005. The

same finding was also observed in this study. The

resource extraction has been aggravated by

population pressure around the interface localities of

SRF. Someone may conclude unsustainable and

illegal harvesting of forest resources is the major

contributory factor for spatial and temporal changes

in the tree cover of SRF. Nevertheless, population

size of Sundarbans impact zone upazila’s (sub

districts) decreased from 22,074,05 in 2001 to

21,558,89 in 2011 (BBS, 2001; BBS, 2011). On the

other hand, population growth rate was also

negative during 2001 to 2011, yet vegetated land

from Sundarbans decreased during 2001 to 2009.

This decreased could be explained by climate

induced disasters. In the last decade (between 2001

and 2011), several cyclones had landfall through

Sundarbans. Cyclone Sidr (landfall on 15 November

2007 with an average wind speed of 223 km per

hour) and cyclone Aila (stuck on 25 May 2009)

were the two most devastated disasters that caused

serious damage to Sundarbans, tress were uprooted

and broken down, thousands of animals of

Sundarbans died, saline water flushed into the forest

causing fresh water scarcity for biotic species. After

the landfalls of those two consecutive cyclones,

impact of climate change has become more

pronounced in the southern coastal zone of

Bangladesh. Therefore, Sundarbans is the innocent

victims of climate induced disasters in many ways.

The notable impacts of climate change on

Sundarbans would be sea level rise, increases in

frequency and intensity of cyclonic storm, irregular

rainfall, salinity ingression, and many more.

The SRF is the single largest contiguous

mangrove forest ecosystem in the world. According

to FAO (2007), the physical boundary of the reserve

forest has changed several times over the years, and

the creation of the protected area ended in

1932/1933, and the limit of the reserved forest has

not changed since then. Thereafter, the Sundarbans

Reserved Forest appears to have been relatively well

protected and the area has been kept relatively intact

(Wilkie and Fortuna, 2003). The study of Rahman

and Begum (2013) found no notable change in

mangrove forest during 1980 to 2009. This study

also revealed that geographical boundary of SRF is

well conserved by the government except changes in

land cover (water bodies, mudflats, barren lands and

vegetated lands) within Sundarbans reserved forest.

4. CONCLUSIONS

The natural forest of Bangladesh has been

declining significantly. The annual loss of forest in

1978 1973 1989 2001 2009 2010

0.0

50000.0

100000.0

150000.0

200000.0

250000.0

300000.0

350000.0

400000.0

450000.0


Bangladesh is estimated around 0.015 million

hectares (Choudhury and Hossain, 2011). The area

under mangrove was 3.35% in 2000 which

increased from 3.12% in 1976 (Hasan et al., 2013).

This increase of mangrove forest was attributed by

mangrove plantation along the shore land of coastal

districts up to 2000 (Hasan et al., 2013).

To understand land cover dynamics of

Sundarbans over last 37 years, four major land

cover classes were categorized-water body, mudflat,

barren lands and vegetated lands. The major finding

of this research work is that the legal forest area

(geographical boundary) of Sundarbans reserved

forest in Bangladesh had not changed during study

period and theoretically, it is now well conserved.

But while analyzing the temporal changes in land

cover classes, this study found significant

fluctuation of different land cover classes. From

1973 to 2010, water bodies, mudflats and barren

lands increased by 0.45%, 19.69% and 14.81%,

respectively; whereas the vegetated lands decreased

by 4.01% during the same period. This indicates that

density of evergreen vegetation and its canopy

closure has been decreasing. The increase in

mudflats was attributed by erosion and deposition in

the forest, while increase in barren lands was due to

both anthropogenic factors (illegal falling, decrease

in freshwater flow) and natural forces (climate

induced severe cyclones, surges and salinity

ingressions).

Hypothetically, if government develops

strong policies and takes effective and efficient legal

measures to conserve Sundarbans from illegal

harvesting, even after Sundarbans will further be

degraded because of climate change impact which

was the major thrust of this research work. The

recommendations of this paper to develop GIS and

RS based regular monitoring system that can easily

identify temporal and spatial changes of forest cover

of Sundarbans. Along with that comprehensive

protection measures should be taken at national and

international level to conserve Sundarbans. Political

commitment and willingness on trans-boundary

river management is also important to protect

Sundarbans from human induced climate change

impacts.

ACKNOWLEDGEMENTS

The authors wish to thanks to all anonymous

researchers and research institutions who published

their scholarly articles on Sundarbans mangrove

forest. The authors would like to thank the

anonymous reviewers for their inputs to the

development of this manuscript.

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spatial and temporal changes of sundarbans reserve forest ... · sundarbans is located south of the...

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